Cluster Computing

, Volume 21, Issue 2, pp 1261–1273 | Cite as

Grid-enabled evolution strategies for large-scale home care crew scheduling

  • Francisco LunaEmail author
  • Alejandro Cervantes
  • Pedro Isasi
  • Juan F. Valenzuela-Valdés


The home care crew scheduling (HCCS) problem is a planning task whose goal is to allocate a set of professional caregivers in the most efficient way to perform a number of assistencial and health care visits to the customers private homes. This is part of an important trend in advanced health care systems, to promote “independent living” specially in situations of dependency on long-term care. This not only ensures a higher quality of life but also a lower cost for society. Real instances of the HCCS problem are large and highly constrained due to both caregivers’ contract limitations and customers’ needs. This paper presents an advanced parallel model that solves HCCS problems using a grid-based asynchronous evolutionary algorithm (EA). Our approach has been tested using a grid computing facility of up to 300 nodes. The algorithm is a modified \((1 + \lambda )\) EA, parallelized using a master/worker model that minimizes communication requirements and processor bottlenecks by distributing both the execution of the EA operators and the evaluation of solutions. We have used three large real-world instances provided by a private company to perform experimentation with different configurations of the EA and number of workers. Results show that our algorithm achieves solutions that clearly outperform the solution provided by the company and the grid-based algorithm is able to handle real world HCCS problems


Home care scheduling Parallelism Grid computing Evolutionary algorithms 



The work of Francisco Luna and Juan F. Valenzuela is funded by the Spanish Ministry of Economy, Industry and Competitiveness under contract TIN2016-75097-P. The work of Alejandro Cervantes and Pedro Isasi is funded by the Spanish Ministry of Science and Innovation under contract TIN2011-28336. We also thank EULEN for their contribution and cooperation for this research.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Francisco Luna
    • 1
    Email author
  • Alejandro Cervantes
    • 2
  • Pedro Isasi
    • 2
  • Juan F. Valenzuela-Valdés
    • 3
  1. 1.Departamento de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain
  2. 2.Departamento de InformáticaUniversidad Carlos III de Madrid, LeganésMadridSpain
  3. 3.Departamento Teoría de la Señal, Telemática y ComunicacionesUniversidad de GranadaGranadaSpain

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